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Nguyen HA, Mai QD, Nguyet Nga DT, Pham MK, Nguyen QK, Do TH, Luong VT, Lam VD, Le AT. Paper/GO/e-Au flexible SERS sensors for in situ detection of tricyclazole in orange juice and on cucumber skin at the sub-ppb level: machine learning-assisted data analysis. NANOSCALE ADVANCES 2024; 6:3106-3118. [PMID: 38868820 PMCID: PMC11166118 DOI: 10.1039/d3na01113e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/23/2024] [Indexed: 06/14/2024]
Abstract
Despite being an excellent surface enhanced Raman scattering (SERS) active material, gold nanoparticles were difficult to be loaded onto the surface of filter paper to fabricate flexible SERS substrates. In this study, electrochemically synthesized gold nanoparticles (e-AuNPs) were deposited on graphene oxide (GO) nanosheets in solution by ultrasonication, resulting in the formation of a GO/Au hybrid material. Thanks to the support of GO, the hybrid material could adhere onto the surface of filter paper, which was immersed into a GO/Au solution for 24 h and dried naturally at room temperature. The paper-based materials were then employed as substrates for a surface enhanced Raman scattering (SERS) sensing platform to detect tricyclazole (TCZ), a widely used pesticide, resulting in better sensitivity compared to the use of paper/Au SERS sensors. With the most optimal GO content of 4%, paper/GO/Au SERS sensors could achieve a limit of detection of 1.32 × 10-10 M in standard solutions. Furthermore, the filter paper-based SERS sensors also exhibited significant advantages in sample collection in real samples. On one hand, the sensors were dipped into orange juice, allowing TCZ molecules in this real sample to be adsorbed onto their SERS active surface. On the other hand, they were pasted onto cucumber skin to collect the analytes. As a result, the paper/GO/Au SERS sensors could sense TCZ in orange juice and on cucumber skin at concentrations as low as 10-9 M (∼2 ppb). In addition, a machine learning model was designed and developed, allowing the sensing system to discriminate TCZ from nine other organic compounds and predict the presence of TCZ on cucumber skin at concentrations down to 10-9 M.
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Affiliation(s)
- Ha Anh Nguyen
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
| | - Quan Doan Mai
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
| | - Dao Thi Nguyet Nga
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
| | - Minh Khanh Pham
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
| | - Quoc Khanh Nguyen
- Faculty of Computer Science, Phenikaa University Hanoi 12116 Vietnam
| | - Trong Hiep Do
- Faculty of Computer Science, Phenikaa University Hanoi 12116 Vietnam
| | - Van Thien Luong
- Faculty of Computer Science, Phenikaa University Hanoi 12116 Vietnam
| | - Vu Dinh Lam
- Institute of Materials Science (IMS), Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Hanoi 10000 Vietnam
| | - Anh-Tuan Le
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
- Faculty of Materials Science and Engineering (MSE), Phenikaa University Hanoi 12116 Vietnam
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2
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Lee S, Dang H, Moon JI, Kim K, Joung Y, Park S, Yu Q, Chen J, Lu M, Chen L, Joo SW, Choo J. SERS-based microdevices for use as in vitro diagnostic biosensors. Chem Soc Rev 2024; 53:5394-5427. [PMID: 38597213 DOI: 10.1039/d3cs01055d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.
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Affiliation(s)
- Sungwoon Lee
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Hajun Dang
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Joung-Il Moon
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Kihyun Kim
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Younju Joung
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Sohyun Park
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Qian Yu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Jiadong Chen
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Mengdan Lu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Lingxin Chen
- School of Pharmacy, Binzhou Medical University, Yantai, 264003, China
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Yantai 264003, China.
| | - Sang-Woo Joo
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul 06978, South Korea.
| | - Jaebum Choo
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
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3
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Zhou H, Li D, Ren Z, Xu C, Wang LF, Lee C. Surface plasmons-phonons for mid-infrared hyperspectral imaging. SCIENCE ADVANCES 2024; 10:eado3179. [PMID: 38809968 PMCID: PMC11135386 DOI: 10.1126/sciadv.ado3179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 04/23/2024] [Indexed: 05/31/2024]
Abstract
Surface plasmons have proven their ability to boost the sensitivity of mid-infrared hyperspectral imaging by enhancing light-matter interactions. Surface phonons, a counterpart technology to plasmons, present unclear contributions to hyperspectral imaging. Here, we investigate this by developing a plasmon-phonon hyperspectral imaging system that uses asymmetric cross-shaped nanoantennas composed of stacked plasmon-phonon materials. The phonon modes within this system, controlled by light polarization, capture molecular refractive index intensity and lineshape features, distinct from those observed with plasmons, enabling more precise and sensitive molecule identification. In a deep learning-assisted imaging demonstration of severe acute respiratory syndrome coronavirus (SARS-CoV), phonons exhibit enhanced identification capabilities (230,400 spectra/s), facilitating the de-overlapping and observation of the spatial distribution of two mixed SARS-CoV spike proteins. In addition, the plasmon-phonon system demonstrates increased identification accuracy (93%), heightened sensitivity, and enhanced detection limits (down to molecule monolayers). These findings extend phonon polaritonics to hyperspectral imaging, promising applications in imaging-guided molecule screening and pharmaceutical analysis.
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Affiliation(s)
- Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117583, Singapore
| | - Dongxiao Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117583, Singapore
| | - Zhihao Ren
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117583, Singapore
| | - Cheng Xu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117583, Singapore
| | - Lin-Fa Wang
- Programme in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117583, Singapore
- NUS Suzhou Research Institute (NUSRI), Suzhou, Jiangsu 215123, China
- NUS Graduate School–Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
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4
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Lee H, Liu KH, Yang YH, Liao JD, Lin BS, Wu ZZ, Chang AC, Tseng CC, Wang MC, Tsai YS. Advances in uremic toxin detection and monitoring in the management of chronic kidney disease progression to end-stage renal disease. Analyst 2024; 149:2784-2795. [PMID: 38647233 DOI: 10.1039/d4an00057a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Patients with end-stage kidney disease (ESKD) rely on dialysis to remove toxins and stay alive. However, hemodialysis alone is insufficient to completely remove all/major uremic toxins, resulting in the accumulation of specific toxins over time. The complexity of uremic toxins and their varying clearance rates across different dialysis modalities poses significant challenges, and innovative approaches such as microfluidics, biomarker discovery, and point-of-care testing are being investigated. This review explores recent advances in the qualitative and quantitative analysis of uremic toxins and highlights the use of innovative methods, particularly label-mediated and label-free surface-enhanced Raman spectroscopy, primarily for qualitative detection. The ability to analyze uremic toxins can optimize hemodialysis settings for more efficient toxin removal. Integration of multiple omics disciplines will also help identify biomarkers and understand the pathogenesis of ESKD, provide deeper understanding of uremic toxin profiling, and offer insights for improving hemodialysis programs. This review also highlights the importance of early detection and improved understanding of chronic kidney disease to improve patient outcomes.
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Affiliation(s)
- Han Lee
- Laboratory of Engineered Materials for Biomedical Applications, Department of Materials Science and Engineering, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan.
| | - Kuan-Hung Liu
- Division of Nephrology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan.
| | - Yu-Hsuan Yang
- Division of Nephrology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan.
| | - Jiunn-Der Liao
- Laboratory of Engineered Materials for Biomedical Applications, Department of Materials Science and Engineering, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan.
| | - Bo-Shen Lin
- Laboratory of Engineered Materials for Biomedical Applications, Department of Materials Science and Engineering, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan.
| | - Zheng-Zhe Wu
- Division of Nephrology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan.
| | - Alice Chinghsuan Chang
- Center for Measurement Standards, Industrial Technology Research Institute, No. 321, Kuang Fu Road, Section 2, Hsinchu 300, Taiwan.
| | - Chin-Chung Tseng
- Division of Nephrology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan.
| | - Ming-Cheng Wang
- Division of Nephrology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan.
| | - Yau-Sheng Tsai
- Center for Clinical Medicine Research, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan.
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Hassanzadeh-Barforoushi A, Tukova A, Nadalini A, Inglis DW, Chang-Hao Tsao S, Wang Y. Microfluidic-SERS Technologies for CTC: A Perspective on Clinical Translation. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38652011 DOI: 10.1021/acsami.4c01158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Enumeration and phenotypic profiling of circulating tumor cells (CTCs) provide critical information for clinical diagnosis and treatment monitoring in cancer. To achieve this goal, an integrated system is needed to efficiently isolate CTCs from patient samples and sensitively evaluate their phenotypes. Such integration would comprise a high-throughput single-cell processing unit for the isolation and manipulation of CTCs and a sensitive and multiplexed quantitation unit to detect clinically relevant signals from these cells. Surface-enhanced Raman scattering (SERS) has been used as an analytical method for molecular profiling and in vitro cancer diagnosis. More recently, its multiplexing capability and power to create distinct molecular signatures against their targets have garnered attention. Here, we share our insights into the combined power of microfluidics and SERS in realizing CTC isolation, enumeration, and detection from a clinical translation perspective. We highlight the key operational factors in CTC microfluidic processing and SERS detection from patient samples. We further discuss microfluidic-SERS integration and its clinical utility as a paradigm shift in clinical CTC-based cancer diagnosis and prognostication. Finally, we summarize the challenges and attempt to look forward to what lies ahead of us in potentially translating the technique into real clinical applications.
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Affiliation(s)
- Amin Hassanzadeh-Barforoushi
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Anastasiia Tukova
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Audrey Nadalini
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| | - David W Inglis
- School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Simon Chang-Hao Tsao
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
- Department of Surgery, Austin Health, University of Melbourne, Heidelberg, Victoria 3084, Australia
| | - Yuling Wang
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
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Liu X, Zhang Z, Zhou J, Liu W, Zhou G, Lee C. Artificial Intelligence-Enhanced Waveguide "Photonic Nose"- Augmented Sensing Platform for VOC Gases in Mid-Infrared. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2400035. [PMID: 38576121 DOI: 10.1002/smll.202400035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/17/2024] [Indexed: 04/06/2024]
Abstract
On-chip nanophotonic waveguide sensor is a promising solution for miniaturization and label-free detection of gas mixtures utilizing the absorption fingerprints in the mid-infrared (MIR) region. However, the quantitative detection and analysis of organic gas mixtures is still challenging and less reported due to the overlapping of the absorption spectrum. Here,an Artificial-Intelligence (AI) assisted waveguide "Photonic nose" is presented as an augmented sensing platform for gas mixture analysis in MIR. With the subwavelength grating cladding supported waveguide design and the help of machine learning algorithms, the MIR absorption spectrum of the binary organic gas mixture is distinguished from arbitrary mixing ratio and decomposed to the single-component spectra for concentration prediction. As a result, the classification of 93.57% for 19 mixing ratios is realized. In addition, the gas mixture spectrum decomposition and concentration prediction show an average root-mean-square error of 2.44 vol%. The work proves the potential for broader sensing and analytical capabilities of the MIR waveguide platform for multiple organic gas components toward MIR on-chip spectroscopy.
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Affiliation(s)
- Xinmiao Liu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117608, Singapore
- Department of Mechanical Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Zixuan Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117608, Singapore
| | - Jingkai Zhou
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117608, Singapore
| | - Weixin Liu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117608, Singapore
| | - Guangya Zhou
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117608, Singapore
- Department of Mechanical Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117608, Singapore
- NUS Suzhou Research Institute (NUSRI), Suzhou, Jiangsu, 215123, China
- NUS Graduate School's Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, 117583, Singapore
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7
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Aswathy R, Sumathi S. The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence. Mol Biotechnol 2024:10.1007/s12033-024-01124-7. [PMID: 38573545 DOI: 10.1007/s12033-024-01124-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 02/15/2024] [Indexed: 04/05/2024]
Abstract
Cervical cancer (CC) continues to be a major worldwide health concern, profoundly impacting the lives of countless females worldwide. In low- and middle-income countries (LMICs), where CC prevalence is high, innovative, and cost-effective approaches for prevention, diagnosis, and treatment are vital. These approaches must ensure high response rates with minimal side effects to improve outcomes. The study aims to compile the latest developments in the field of CC, providing insights into the promising future of CC management along with the research gaps and challenges. Integrating biotechnology and artificial intelligence (AI) holds immense potential to revolutionize CC care, from MobileODT screening to precision medicine and innovative therapies. AI enhances healthcare accuracy and improves patient outcomes, especially in CC screening, where its use has increased over the years, showing promising results. Also, combining newly developed strategies with conventional treatment options presents an optimal approach to address the limitations associated with conventional methods. However, further clinical studies are essential for practically implementing these advancements in society. By leveraging these cutting-edge technologies and approaches, there is a substantial opportunity to reduce the global burden of this preventable malignancy, ultimately improving the lives of women in LMICs and beyond.
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Affiliation(s)
- Raghu Aswathy
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Bharathi Park Rd, Near Forest College Campus, Saibaba Colony, Coimbatore, Tamil Nadu, 641043, India
| | - Sundaravadivelu Sumathi
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Bharathi Park Rd, Near Forest College Campus, Saibaba Colony, Coimbatore, Tamil Nadu, 641043, India.
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Vázquez-Iglesias L, Stanfoca Casagrande GM, García-Lojo D, Ferro Leal L, Ngo TA, Pérez-Juste J, Reis RM, Kant K, Pastoriza-Santos I. SERS sensing for cancer biomarker: Approaches and directions. Bioact Mater 2024; 34:248-268. [PMID: 38260819 PMCID: PMC10801148 DOI: 10.1016/j.bioactmat.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/14/2023] [Accepted: 12/21/2023] [Indexed: 01/24/2024] Open
Abstract
These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.
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Affiliation(s)
- Lorena Vázquez-Iglesias
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | | | - Daniel García-Lojo
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Letícia Ferro Leal
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil
- Barretos School of Medicine Dr. Paulo Prata—FACISB, Barretos, 14785-002, Brazil
| | - Tien Anh Ngo
- Vinmec Tissue Bank, Vinmec Health Care System, Hanoi, Viet Nam
| | - Jorge Pérez-Juste
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Rui Manuel Reis
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil
- Life and Health Sciences Research Institute (ICVS), School of Medicine, Campus de Gualtar, University of Minho, 4710-057 Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, 4710-057, Braga, Portugal
| | - Krishna Kant
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Isabel Pastoriza-Santos
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
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9
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Li D, Yadav A, Zhou H, Roy K, Thanasekaran P, Lee C. Advances and Applications of Metal-Organic Frameworks (MOFs) in Emerging Technologies: A Comprehensive Review. GLOBAL CHALLENGES (HOBOKEN, NJ) 2024; 8:2300244. [PMID: 38356684 PMCID: PMC10862192 DOI: 10.1002/gch2.202300244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/19/2023] [Indexed: 02/16/2024]
Abstract
Metal-organic frameworks (MOFs) that are the wonder material of the 21st century consist of metal ions/clusters coordinated to organic ligands to form one- or more-dimensional porous structures with unprecedented chemical and structural tunability, exceptional thermal stability, ultrahigh porosity, and a large surface area, making them an ideal candidate for numerous potential applications. In this work, the recent progress in the design and synthetic approaches of MOFs and explore their potential applications in the fields of gas storage and separation, catalysis, magnetism, drug delivery, chemical/biosensing, supercapacitors, rechargeable batteries and self-powered wearable sensors based on piezoelectric and triboelectric nanogenerators are summarized. Lastly, this work identifies present challenges and outlines future opportunities in this field, which can provide valuable references.
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Affiliation(s)
- Dongxiao Li
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore117583Singapore
- Center for Intelligent Sensors and MEMSNational University of SingaporeSingapore117608Singapore
| | - Anurag Yadav
- Department of ChemistryPondicherry UniversityPuducherry605014India
| | - Hong Zhou
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore117583Singapore
- Center for Intelligent Sensors and MEMSNational University of SingaporeSingapore117608Singapore
| | - Kaustav Roy
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore117583Singapore
- Center for Intelligent Sensors and MEMSNational University of SingaporeSingapore117608Singapore
| | | | - Chengkuo Lee
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore117583Singapore
- Center for Intelligent Sensors and MEMSNational University of SingaporeSingapore117608Singapore
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10
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Xie M, Zhu Y, Li Z, Yan Y, Liu Y, Wu W, Zhang T, Li Z, Wang H. Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis. Talanta 2024; 268:125281. [PMID: 37832450 DOI: 10.1016/j.talanta.2023.125281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/09/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023]
Abstract
Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.
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Affiliation(s)
- Maomei Xie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yiting Zhu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zhiyao Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yueling Yan
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yidan Liu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Wenbo Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Tong Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
| | - Haixia Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
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11
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Zhao Y, Kumar A, Yang Y. Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates. Chem Soc Rev 2024; 53:1004-1057. [PMID: 38116610 DOI: 10.1039/d3cs00540b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Recently, there has been an exponential growth in the number of publications focusing on surface-enhanced Raman scattering (SERS), primarily driven by advancements in nanotechnology and the increasing demand for chemical and biological detection. While many of these publications have focused on the development of new substrates and detection-based applications, there is a noticeable lack of attention given to various practical issues related to SERS measurements and detection. This review aims to fill this gap by utilizing silver nanorod (AgNR) SERS substrates fabricated through the oblique angle deposition method as an illustrative example. The review highlights and addresses a range of practical issues associated with SERS measurements and detection. These include the optimization of SERS substrates in terms of morphology and structural design, considerations for measurement configurations such as polarization and the incident angle of the excitation laser, and exploration of enhancement mechanisms encompassing both intrinsic properties induced by the structure and materials, as well as extrinsic factors arising from wetting/dewetting phenomena and analyte size. The manufacturing and storage aspects of SERS substrates, including scalable fabrication techniques, contamination control, cleaning procedures, and appropriate storage methods, are also discussed. Furthermore, the review delves into device design considerations, such as well arrays, flow cells, and fiber probes, and explores various sample preparation methods such as drop-cast and immersion. Measurement issues, including the effect of excitation laser wavelength and power, as well as the influence of buffer, are thoroughly examined. Additionally, the review discusses spectral analysis techniques, encompassing baseline removal, chemometric analysis, and machine learning approaches. The wide range of AgNR-based applications of SERS, across various fields, is also explored. Throughout the comprehensive review, key lessons learned from collective findings are outlined and analyzed, particularly in the context of detailed SERS measurements and standardization. The review also provides insights into future challenges and perspectives in the field of SERS. It is our hope that this comprehensive review will serve as a valuable reference for researchers seeking to embark on in-depth studies and applications involving their own SERS substrates.
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Affiliation(s)
- Yiping Zhao
- Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
| | - Amit Kumar
- Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
| | - Yanjun Yang
- School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
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12
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Zhou H, Ren Z, Li D, Xu C, Mu X, Lee C. Dynamic construction of refractive index-dependent vibrations using surface plasmon-phonon polaritons. Nat Commun 2023; 14:7316. [PMID: 37952033 PMCID: PMC10640644 DOI: 10.1038/s41467-023-43127-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/01/2023] [Indexed: 11/14/2023] Open
Abstract
One of the fundamental hurdles in infrared spectroscopy is the failure of molecular identification when their infrared vibrational fingerprints overlap. Refractive index (RI) is another intrinsic property of molecules associated with electronic polarizability, but with limited contribution to molecular identification in mixed environments currently. Here, we investigate the coupling mode of localized surface plasmon and surface phonon polaritons for vibrational de-overlapping. The coupling mode is sensitive to the molecular refractive index, attributed to the RI-induced vibrational variations of surface phonon polaritons (SPhP) within the Reststrahlen band, referred to as RI-dependent SPhP vibrations. The RI-dependent SPhP vibrations are linked to molecular RI features. According to the deep-learning-augmented demonstration of bond-breaking-bond-making dynamic profiling in biological reaction, we substantiate that the RI-dependent SPhP vibrations effectively disentangle overlapping vibrational modes, achieving a 92% identification accuracy even for the strongly overlapping vibrational modes in the reaction. Our findings offer insights into the realm of light-matter interaction and provide a valuable toolkit for biomedicine applications.
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Affiliation(s)
- Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117583, Singapore
| | - Zhihao Ren
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117583, Singapore
| | - Dongxiao Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117583, Singapore
| | - Cheng Xu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117583, Singapore
| | - Xiaojing Mu
- Key Laboratory of Optoelectronic Technology & Systems of Ministry of Education, International R&D Center of Micro-Nano Systems and New Materials Technology, Chongqing University, Chongqing, 400044, P. R. China.
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117583, Singapore.
- NUS Suzhou Research Institute (NUSRI), Suzhou, Jiangsu, 215123, China.
- NUS Graduate School-Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, 119077, Singapore.
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13
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Li D, Xu C, Xie J, Lee C. Research Progress in Surface-Enhanced Infrared Absorption Spectroscopy: From Performance Optimization, Sensing Applications, to System Integration. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2377. [PMID: 37630962 PMCID: PMC10458771 DOI: 10.3390/nano13162377] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023]
Abstract
Infrared absorption spectroscopy is an effective tool for the detection and identification of molecules. However, its application is limited by the low infrared absorption cross-section of the molecule, resulting in low sensitivity and a poor signal-to-noise ratio. Surface-Enhanced Infrared Absorption (SEIRA) spectroscopy is a breakthrough technique that exploits the field-enhancing properties of periodic nanostructures to amplify the vibrational signals of trace molecules. The fascinating properties of SEIRA technology have aroused great interest, driving diverse sensing applications. In this review, we first discuss three ways for SEIRA performance optimization, including material selection, sensitivity enhancement, and bandwidth improvement. Subsequently, we discuss the potential applications of SEIRA technology in fields such as biomedicine and environmental monitoring. In recent years, we have ushered in a new era characterized by the Internet of Things, sensor networks, and wearable devices. These new demands spurred the pursuit of miniaturized and consolidated infrared spectroscopy systems and chips. In addition, the rise of machine learning has injected new vitality into SEIRA, bringing smart device design and data analysis to the foreground. The final section of this review explores the anticipated trajectory that SEIRA technology might take, highlighting future trends and possibilities.
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Affiliation(s)
- Dongxiao Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore; (D.L.); (C.X.); (J.X.)
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
| | - Cheng Xu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore; (D.L.); (C.X.); (J.X.)
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
| | - Junsheng Xie
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore; (D.L.); (C.X.); (J.X.)
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore; (D.L.); (C.X.); (J.X.)
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- NUS Suzhou Research Institute (NUSRI), Suzhou 215123, China
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14
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Wang C, He T, Zhou H, Zhang Z, Lee C. Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform. Bioelectron Med 2023; 9:17. [PMID: 37528436 PMCID: PMC10394931 DOI: 10.1186/s42234-023-00118-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/17/2023] [Indexed: 08/03/2023] Open
Abstract
The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.
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Affiliation(s)
- Chan Wang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Tianyiyi He
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Zixuan Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore.
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore.
- NUS Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou, 215123, China.
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 117456, Singapore.
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15
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Brandt S, Pavlichenko I, Shneidman AV, Patel H, Tripp A, Wong TSB, Lazaro S, Thompson E, Maltz A, Storwick T, Beggs H, Szendrei-Temesi K, Lotsch BV, Kaplan CN, Visser CW, Brenner MP, Murthy VN, Aizenberg J. Nonequilibrium sensing of volatile compounds using active and passive analyte delivery. Proc Natl Acad Sci U S A 2023; 120:e2303928120. [PMID: 37494398 PMCID: PMC10400973 DOI: 10.1073/pnas.2303928120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/22/2023] [Indexed: 07/28/2023] Open
Abstract
Although sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch, the development of artificial noses is significantly behind their biological counterparts. This largely stems from the sophistication of natural olfaction, which relies on both fluid dynamics within the nasal anatomy and the response patterns of hundreds to thousands of unique molecular-scale receptors. We designed a sensing approach to identify volatiles inspired by the fluid dynamics of the nose, allowing us to extract information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) rather than relying on a large sensor array. By accentuating differences in the nonequilibrium mass-transport dynamics of vapors and training a machine learning algorithm on the sensor output, we clearly identified polar and nonpolar volatile compounds, determined the mixing ratios of binary mixtures, and accurately predicted the boiling point, flash point, vapor pressure, and viscosity of a number of volatile liquids, including several that had not been used for training the model. We further implemented a bioinspired active sniffing approach, in which the analyte delivery was performed in well-controlled 'inhale-exhale' sequences, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. Our results outline a strategy to build accurate and rapid artificial noses for volatile compounds that can provide useful information such as the composition and physical properties of chemicals, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.
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Affiliation(s)
- Soeren Brandt
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA02134
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Ida Pavlichenko
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA02134
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Anna V. Shneidman
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA02134
| | - Haritosh Patel
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA02134
| | - Austin Tripp
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Timothy S. B. Wong
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Sean Lazaro
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Ethan Thompson
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Aubrey Maltz
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Thomas Storwick
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Holden Beggs
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
| | - Katalin Szendrei-Temesi
- Max Planck Institute for Solid State Research, Stuttgart70569, Germany
- Department of Chemistry, Ludwig-Maximilians-Universität München, München81377, Germany
| | - Bettina V. Lotsch
- Max Planck Institute for Solid State Research, Stuttgart70569, Germany
- Department of Chemistry, Ludwig-Maximilians-Universität München, München81377, Germany
| | - C. Nadir Kaplan
- Department of Physics, Virginia Polytechnic Institute and State University, Blacksburg, VA24061
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University, Blacksburg, VA24061
| | - Claas W. Visser
- Department of Thermal and Fluid Engineering, Faculty of Engineering Technology, University of Twente, Enschede7522 NB, Netherlands
| | - Michael P. Brenner
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA02134
| | - Venkatesh N. Murthy
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA02138
- Center for Brain Science, Harvard University, Cambridge, MA02138
| | - Joanna Aizenberg
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA02134
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA02138
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA02138
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16
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Beeram R, Vepa KR, Soma VR. Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques. BIOSENSORS 2023; 13:328. [PMID: 36979540 PMCID: PMC10046859 DOI: 10.3390/bios13030328] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.
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17
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Zhou J, Zhang Z, Dong B, Ren Z, Liu W, Lee C. Midinfrared Spectroscopic Analysis of Aqueous Mixtures Using Artificial-Intelligence-Enhanced Metamaterial Waveguide Sensing Platform. ACS NANO 2023; 17:711-724. [PMID: 36576121 DOI: 10.1021/acsnano.2c10163] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
As miniaturized solutions, mid-infrared (MIR) waveguide sensors are promising for label-free compositional detection of mixtures leveraging plentiful absorption fingerprints. However, the quantitative analysis of liquid mixtures is still challenging using MIR waveguide sensors, as the absorption spectrum overlaps for multiple organic components accompanied by strong water absorption background. Here, we present an artificial-intelligence-enhanced metamaterial waveguide sensing platform (AIMWSP) for aqueous mixture analysis in the MIR. With the sensitivity-improved metamaterial waveguide and assistance of machine learning, the MIR absorption spectra of a ternary mixture in water can be successfully distinguished and decomposed to single-component spectra for predicting concentration. A classification accuracy of 98.88% for 64 mixing ratios and 92.86% for four concentrations below the limit of detection (972 ppm, based on 3σ) with steps of 300 ppm are realized. Besides, the mixture concentration prediction with root-mean-squared error varying from 0.107 vol % to 1.436 vol % is also achieved. Our work indicates the potential of further extending this sensing platform to MIR spectrometer-on-chip aiming for the data analytics of multiple organic components in aqueous environments.
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Affiliation(s)
- Jingkai Zhou
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, Singapore117608
| | - Zixuan Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, Singapore117608
| | - Bowei Dong
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, Singapore117608
| | - Zhihao Ren
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, Singapore117608
| | - Weixin Liu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, Singapore117608
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, Singapore117608
- NUS Graduate School - Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore119077
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